Spaces:
Runtime error
Runtime error
import torch | |
from torch import nn | |
from torch.nn import functional as F | |
import modules | |
import attentions | |
from torch.nn import Conv1d, ConvTranspose1d, Conv2d | |
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm | |
from commons import init_weights, get_padding | |
import torchaudio | |
from einops import rearrange | |
import transformers | |
import math | |
from styleencoder import StyleEncoder | |
import commons | |
from alias_free_torch import * | |
import activations | |
class Wav2vec2(torch.nn.Module): | |
def __init__(self, layer=7, w2v='mms'): | |
"""we use the intermediate features of mms-300m. | |
More specifically, we used the output from the 7th layer of the 24-layer transformer encoder. | |
""" | |
super().__init__() | |
if w2v == 'mms': | |
self.wav2vec2 = transformers.Wav2Vec2ForPreTraining.from_pretrained("facebook/mms-300m") | |
else: | |
self.wav2vec2 = transformers.Wav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-xls-r-300m") | |
for param in self.wav2vec2.parameters(): | |
param.requires_grad = False | |
param.grad = None | |
self.wav2vec2.eval() | |
self.feature_layer = layer | |
def forward(self, x): | |
""" | |
Args: | |
x: torch.Tensor of shape (B x t) | |
Returns: | |
y: torch.Tensor of shape(B x C x t) | |
""" | |
outputs = self.wav2vec2(x.squeeze(1), output_hidden_states=True) | |
y = outputs.hidden_states[self.feature_layer] # B x t x C(1024) | |
y = y.permute((0, 2, 1)) # B x t x C -> B x C x t | |
return y | |
class ResidualCouplingBlock_Transformer(nn.Module): | |
def __init__(self, | |
channels, | |
hidden_channels, | |
kernel_size, | |
dilation_rate, | |
n_layers=3, | |
n_flows=4, | |
gin_channels=0): | |
super().__init__() | |
self.channels = channels | |
self.hidden_channels = hidden_channels | |
self.kernel_size = kernel_size | |
self.dilation_rate = dilation_rate | |
self.n_layers = n_layers | |
self.n_flows = n_flows | |
self.gin_channels = gin_channels | |
self.cond_block = torch.nn.Sequential(torch.nn.Linear(gin_channels, 4 * hidden_channels), | |
nn.SiLU(), torch.nn.Linear(4 * hidden_channels, hidden_channels)) | |
self.flows = nn.ModuleList() | |
for i in range(n_flows): | |
self.flows.append(modules.ResidualCouplingLayer_Transformer_simple(channels, hidden_channels, kernel_size, dilation_rate, n_layers, mean_only=True)) | |
self.flows.append(modules.Flip()) | |
def forward(self, x, x_mask, g=None, reverse=False): | |
g = self.cond_block(g.squeeze(2)) | |
if not reverse: | |
for flow in self.flows: | |
x, _ = flow(x, x_mask, g=g, reverse=reverse) | |
else: | |
for flow in reversed(self.flows): | |
x = flow(x, x_mask, g=g, reverse=reverse) | |
return x | |
class PosteriorAudioEncoder(nn.Module): | |
def __init__(self, | |
in_channels, | |
out_channels, | |
hidden_channels, | |
kernel_size, | |
dilation_rate, | |
n_layers, | |
gin_channels=0): | |
super().__init__() | |
self.in_channels = in_channels | |
self.out_channels = out_channels | |
self.hidden_channels = hidden_channels | |
self.kernel_size = kernel_size | |
self.dilation_rate = dilation_rate | |
self.n_layers = n_layers | |
self.gin_channels = gin_channels | |
self.pre = nn.Conv1d(in_channels, hidden_channels, 1) | |
self.down_pre = nn.Conv1d(1, 16, 7, 1, padding=3) | |
self.resblocks = nn.ModuleList() | |
downsample_rates = [8,5,4,2] | |
downsample_kernel_sizes = [17, 10, 8, 4] | |
ch = [16, 32, 64, 128, 192] | |
resblock = AMPBlock1 | |
resblock_kernel_sizes = [3,7,11] | |
resblock_dilation_sizes = [[1,3,5], [1,3,5], [1,3,5]] | |
self.num_kernels = 3 | |
self.downs = nn.ModuleList() | |
for i, (u, k) in enumerate(zip(downsample_rates, downsample_kernel_sizes)): | |
self.downs.append(weight_norm( | |
Conv1d(ch[i], ch[i+1], k, u, padding=(k-1)//2))) | |
for i in range(4): | |
for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): | |
self.resblocks.append(resblock(ch[i+1], k, d, activation="snakebeta")) | |
activation_post = activations.SnakeBeta(ch[i+1], alpha_logscale=True) | |
self.activation_post = Activation1d(activation=activation_post) | |
self.conv_post = Conv1d(ch[i+1], hidden_channels, 7, 1, padding=3) | |
self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) | |
self.proj = nn.Conv1d(hidden_channels*2, out_channels * 2, 1) | |
def forward(self, x, x_audio, x_mask, g=None): | |
x_audio = self.down_pre(x_audio) | |
for i in range(4): | |
x_audio = self.downs[i](x_audio) | |
xs = None | |
for j in range(self.num_kernels): | |
if xs is None: | |
xs = self.resblocks[i*self.num_kernels+j](x_audio) | |
else: | |
xs += self.resblocks[i*self.num_kernels+j](x_audio) | |
x_audio = xs / self.num_kernels | |
x_audio = self.activation_post(x_audio) | |
x_audio = self.conv_post(x_audio) | |
x = self.pre(x) * x_mask | |
x = self.enc(x, x_mask, g=g) | |
x_audio = x_audio * x_mask | |
x = torch.cat([x, x_audio], dim=1) | |
stats = self.proj(x) * x_mask | |
m, logs = torch.split(stats, self.out_channels, dim=1) | |
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask | |
return z, m, logs | |
class PosteriorSFEncoder(nn.Module): | |
def __init__(self, | |
src_channels, | |
out_channels, | |
hidden_channels, | |
kernel_size, | |
dilation_rate, | |
n_layers, | |
gin_channels=0): | |
super().__init__() | |
self.out_channels = out_channels | |
self.hidden_channels = hidden_channels | |
self.kernel_size = kernel_size | |
self.dilation_rate = dilation_rate | |
self.n_layers = n_layers | |
self.gin_channels = gin_channels | |
self.pre_source = nn.Conv1d(src_channels, hidden_channels, 1) | |
self.pre_filter = nn.Conv1d(1, hidden_channels, kernel_size=9, stride=4, padding=4) | |
self.source_enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers//2, gin_channels=gin_channels) | |
self.filter_enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers//2, gin_channels=gin_channels) | |
self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers//2, gin_channels=gin_channels) | |
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) | |
def forward(self, x_src, x_ftr, x_mask, g=None): | |
x_src = self.pre_source(x_src) * x_mask | |
x_ftr = self.pre_filter(x_ftr) * x_mask | |
x_src = self.source_enc(x_src, x_mask, g=g) | |
x_ftr = self.filter_enc(x_ftr, x_mask, g=g) | |
x = self.enc(x_src+x_ftr, x_mask, g=g) | |
stats = self.proj(x) * x_mask | |
m, logs = torch.split(stats, self.out_channels, dim=1) | |
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask | |
return z, m, logs | |
class MelDecoder(nn.Module): | |
def __init__(self, | |
hidden_channels, | |
filter_channels, | |
n_heads, | |
n_layers, | |
kernel_size, | |
p_dropout, | |
mel_size=20, | |
gin_channels=0): | |
super().__init__() | |
self.hidden_channels = hidden_channels | |
self.filter_channels = filter_channels | |
self.n_heads = n_heads | |
self.n_layers = n_layers | |
self.kernel_size = kernel_size | |
self.p_dropout = p_dropout | |
self.conv_pre = Conv1d(hidden_channels, hidden_channels, 3, 1, padding=1) | |
self.encoder = attentions.Encoder( | |
hidden_channels, | |
filter_channels, | |
n_heads, | |
n_layers, | |
kernel_size, | |
p_dropout) | |
self.proj= nn.Conv1d(hidden_channels, mel_size, 1, bias=False) | |
if gin_channels != 0: | |
self.cond = nn.Conv1d(gin_channels, hidden_channels, 1) | |
def forward(self, x, x_mask, g=None): | |
x = self.conv_pre(x*x_mask) | |
if g is not None: | |
x = x + self.cond(g) | |
x = self.encoder(x * x_mask, x_mask) | |
x = self.proj(x) * x_mask | |
return x | |
class SourceNetwork(nn.Module): | |
def __init__(self, upsample_initial_channel=256): | |
super().__init__() | |
resblock_kernel_sizes = [3,5,7] | |
upsample_rates = [2,2] | |
initial_channel = 192 | |
upsample_initial_channel = upsample_initial_channel | |
upsample_kernel_sizes = [4,4] | |
resblock_dilation_sizes = [[1,3,5], [1,3,5], [1,3,5]] | |
self.num_kernels = len(resblock_kernel_sizes) | |
self.num_upsamples = len(upsample_rates) | |
self.conv_pre = weight_norm(Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)) | |
resblock = AMPBlock1 | |
self.ups = nn.ModuleList() | |
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): | |
self.ups.append(weight_norm( | |
ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)), | |
k, u, padding=(k-u)//2))) | |
self.resblocks = nn.ModuleList() | |
for i in range(len(self.ups)): | |
ch = upsample_initial_channel//(2**(i+1)) | |
for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): | |
self.resblocks.append(resblock(ch, k, d, activation="snakebeta")) | |
activation_post = activations.SnakeBeta(ch, alpha_logscale=True) | |
self.activation_post = Activation1d(activation=activation_post) | |
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) | |
self.cond = Conv1d(256, upsample_initial_channel, 1) | |
self.ups.apply(init_weights) | |
def forward(self, x, g): | |
x = self.conv_pre(x) + self.cond(g) | |
for i in range(self.num_upsamples): | |
x = self.ups[i](x) | |
xs = None | |
for j in range(self.num_kernels): | |
if xs is None: | |
xs = self.resblocks[i*self.num_kernels+j](x) | |
else: | |
xs += self.resblocks[i*self.num_kernels+j](x) | |
x = xs / self.num_kernels | |
x = self.activation_post(x) | |
## Predictor | |
x_ = self.conv_post(x) | |
return x, x_ | |
def remove_weight_norm(self): | |
print('Removing weight norm...') | |
for l in self.ups: | |
remove_weight_norm(l) | |
for l in self.resblocks: | |
l.remove_weight_norm() | |
class DBlock(nn.Module): | |
def __init__(self, input_size, hidden_size, factor): | |
super().__init__() | |
self.factor = factor | |
self.residual_dense = weight_norm(Conv1d(input_size, hidden_size, 1)) | |
self.conv = nn.ModuleList([ | |
weight_norm(Conv1d(input_size, hidden_size, 3, dilation=1, padding=1)), | |
weight_norm(Conv1d(hidden_size, hidden_size, 3, dilation=2, padding=2)), | |
weight_norm(Conv1d(hidden_size, hidden_size, 3, dilation=4, padding=4)), | |
]) | |
self.conv.apply(init_weights) | |
def forward(self, x): | |
size = x.shape[-1] // self.factor | |
residual = self.residual_dense(x) | |
residual = F.interpolate(residual, size=size) | |
x = F.interpolate(x, size=size) | |
for layer in self.conv: | |
x = F.leaky_relu(x, modules.LRELU_SLOPE) | |
x = layer(x) | |
return x + residual | |
def remove_weight_norm(self): | |
for l in self.conv: | |
remove_weight_norm(l) | |
class AMPBlock1(torch.nn.Module): | |
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), activation=None): | |
super(AMPBlock1, self).__init__() | |
self.convs1 = nn.ModuleList([ | |
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], | |
padding=get_padding(kernel_size, dilation[0]))), | |
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], | |
padding=get_padding(kernel_size, dilation[1]))), | |
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], | |
padding=get_padding(kernel_size, dilation[2]))) | |
]) | |
self.convs1.apply(init_weights) | |
self.convs2 = nn.ModuleList([ | |
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, | |
padding=get_padding(kernel_size, 1))), | |
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, | |
padding=get_padding(kernel_size, 1))), | |
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, | |
padding=get_padding(kernel_size, 1))) | |
]) | |
self.convs2.apply(init_weights) | |
self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers | |
self.activations = nn.ModuleList([ | |
Activation1d( | |
activation=activations.SnakeBeta(channels, alpha_logscale=True)) | |
for _ in range(self.num_layers) | |
]) | |
def forward(self, x): | |
acts1, acts2 = self.activations[::2], self.activations[1::2] | |
for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2): | |
xt = a1(x) | |
xt = c1(xt) | |
xt = a2(xt) | |
xt = c2(xt) | |
x = xt + x | |
return x | |
def remove_weight_norm(self): | |
for l in self.convs1: | |
remove_weight_norm(l) | |
for l in self.convs2: | |
remove_weight_norm(l) | |
class Generator(torch.nn.Module): | |
def __init__(self, initial_channel, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=256): | |
super(Generator, self).__init__() | |
self.num_kernels = len(resblock_kernel_sizes) | |
self.num_upsamples = len(upsample_rates) | |
self.conv_pre = weight_norm(Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)) | |
resblock = AMPBlock1 | |
self.ups = nn.ModuleList() | |
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): | |
self.ups.append(weight_norm( | |
ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)), | |
k, u, padding=(k-u)//2))) | |
self.resblocks = nn.ModuleList() | |
for i in range(len(self.ups)): | |
ch = upsample_initial_channel//(2**(i+1)) | |
for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): | |
self.resblocks.append(resblock(ch, k, d, activation="snakebeta")) | |
activation_post = activations.SnakeBeta(ch, alpha_logscale=True) | |
self.activation_post = Activation1d(activation=activation_post) | |
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) | |
self.ups.apply(init_weights) | |
if gin_channels != 0: | |
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) | |
self.downs = DBlock(upsample_initial_channel//8, upsample_initial_channel, 4) | |
self.proj = Conv1d(upsample_initial_channel//8, upsample_initial_channel//2, 7, 1, padding=3) | |
def forward(self, x, pitch, g=None): | |
x = self.conv_pre(x) + self.downs(pitch) + self.cond(g) | |
for i in range(self.num_upsamples): | |
x = self.ups[i](x) | |
if i == 0: | |
pitch = self.proj(pitch) | |
x = x + pitch | |
xs = None | |
for j in range(self.num_kernels): | |
if xs is None: | |
xs = self.resblocks[i*self.num_kernels+j](x) | |
else: | |
xs += self.resblocks[i*self.num_kernels+j](x) | |
x = xs / self.num_kernels | |
x = self.activation_post(x) | |
x = self.conv_post(x) | |
x = torch.tanh(x) | |
return x | |
def remove_weight_norm(self): | |
print('Removing weight norm...') | |
for l in self.ups: | |
remove_weight_norm(l) | |
for l in self.resblocks: | |
l.remove_weight_norm() | |
for l in self.downs: | |
l.remove_weight_norm() | |
remove_weight_norm(self.conv_pre) | |
class DiscriminatorP(torch.nn.Module): | |
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): | |
super(DiscriminatorP, self).__init__() | |
self.period = period | |
self.use_spectral_norm = use_spectral_norm | |
norm_f = weight_norm if use_spectral_norm == False else spectral_norm | |
self.convs = nn.ModuleList([ | |
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), | |
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), | |
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), | |
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), | |
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))), | |
]) | |
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) | |
def forward(self, x): | |
fmap = [] | |
# 1d to 2d | |
b, c, t = x.shape | |
if t % self.period != 0: # pad first | |
n_pad = self.period - (t % self.period) | |
x = F.pad(x, (0, n_pad), "reflect") | |
t = t + n_pad | |
x = x.view(b, c, t // self.period, self.period) | |
for l in self.convs: | |
x = l(x) | |
x = F.leaky_relu(x, modules.LRELU_SLOPE) | |
fmap.append(x) | |
x = self.conv_post(x) | |
fmap.append(x) | |
x = torch.flatten(x, 1, -1) | |
return x, fmap | |
class DiscriminatorR(torch.nn.Module): | |
def __init__(self, resolution, use_spectral_norm=False): | |
super(DiscriminatorR, self).__init__() | |
norm_f = weight_norm if use_spectral_norm == False else spectral_norm | |
n_fft, hop_length, win_length = resolution | |
self.spec_transform = torchaudio.transforms.Spectrogram( | |
n_fft=n_fft, hop_length=hop_length, win_length=win_length, window_fn=torch.hann_window, | |
normalized=True, center=False, pad_mode=None, power=None) | |
self.convs = nn.ModuleList([ | |
norm_f(nn.Conv2d(2, 32, (3, 9), padding=(1, 4))), | |
norm_f(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), padding=(1, 4))), | |
norm_f(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), dilation=(2,1), padding=(2, 4))), | |
norm_f(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), dilation=(4,1), padding=(4, 4))), | |
norm_f(nn.Conv2d(32, 32, (3, 3), padding=(1, 1))), | |
]) | |
self.conv_post = norm_f(nn.Conv2d(32, 1, (3, 3), padding=(1, 1))) | |
def forward(self, y): | |
fmap = [] | |
x = self.spec_transform(y) # [B, 2, Freq, Frames, 2] | |
x = torch.cat([x.real, x.imag], dim=1) | |
x = rearrange(x, 'b c w t -> b c t w') | |
for l in self.convs: | |
x = l(x) | |
x = F.leaky_relu(x, modules.LRELU_SLOPE) | |
fmap.append(x) | |
x = self.conv_post(x) | |
fmap.append(x) | |
x = torch.flatten(x, 1, -1) | |
return x, fmap | |
class MultiPeriodDiscriminator(torch.nn.Module): | |
def __init__(self, use_spectral_norm=False): | |
super(MultiPeriodDiscriminator, self).__init__() | |
periods = [2,3,5,7,11] | |
resolutions = [[2048, 512, 2048], [1024, 256, 1024], [512, 128, 512], [256, 64, 256], [128, 32, 128]] | |
discs = [DiscriminatorR(resolutions[i], use_spectral_norm=use_spectral_norm) for i in range(len(resolutions))] | |
discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods] | |
self.discriminators = nn.ModuleList(discs) | |
def forward(self, y, y_hat): | |
y_d_rs = [] | |
y_d_gs = [] | |
fmap_rs = [] | |
fmap_gs = [] | |
for i, d in enumerate(self.discriminators): | |
y_d_r, fmap_r = d(y) | |
y_d_g, fmap_g = d(y_hat) | |
y_d_rs.append(y_d_r) | |
y_d_gs.append(y_d_g) | |
fmap_rs.append(fmap_r) | |
fmap_gs.append(fmap_g) | |
return y_d_rs, y_d_gs, fmap_rs, fmap_gs | |
class SynthesizerTrn(nn.Module): | |
""" | |
Synthesizer for Training | |
""" | |
def __init__(self, | |
spec_channels, | |
segment_size, | |
inter_channels, | |
hidden_channels, | |
filter_channels, | |
n_heads, | |
n_layers, | |
kernel_size, | |
p_dropout, | |
resblock, | |
resblock_kernel_sizes, | |
resblock_dilation_sizes, | |
upsample_rates, | |
upsample_initial_channel, | |
upsample_kernel_sizes, | |
gin_channels=256, | |
prosody_size=20, | |
uncond_ratio=0., | |
cfg=False, | |
**kwargs): | |
super().__init__() | |
self.spec_channels = spec_channels | |
self.inter_channels = inter_channels | |
self.hidden_channels = hidden_channels | |
self.filter_channels = filter_channels | |
self.n_heads = n_heads | |
self.n_layers = n_layers | |
self.kernel_size = kernel_size | |
self.p_dropout = p_dropout | |
self.resblock = resblock | |
self.resblock_kernel_sizes = resblock_kernel_sizes | |
self.resblock_dilation_sizes = resblock_dilation_sizes | |
self.upsample_rates = upsample_rates | |
self.upsample_initial_channel = upsample_initial_channel | |
self.upsample_kernel_sizes = upsample_kernel_sizes | |
self.segment_size = segment_size | |
self.mel_size = prosody_size | |
self.enc_p_l = PosteriorSFEncoder(1024, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) | |
self.flow_l = ResidualCouplingBlock_Transformer(inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels) | |
self.enc_p = PosteriorSFEncoder(1024, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) | |
self.enc_q = PosteriorAudioEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) | |
self.flow = ResidualCouplingBlock_Transformer(inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels) | |
self.mel_decoder = MelDecoder(inter_channels, | |
filter_channels, | |
n_heads=2, | |
n_layers=2, | |
kernel_size=5, | |
p_dropout=0.1, | |
mel_size=self.mel_size, | |
gin_channels=gin_channels) | |
self.dec = Generator(inter_channels, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels) | |
self.sn = SourceNetwork(upsample_initial_channel//2) | |
self.emb_g = StyleEncoder(in_dim=80, hidden_dim=256, out_dim=gin_channels) | |
if cfg: | |
self.emb = torch.nn.Embedding(1, 256) | |
torch.nn.init.normal_(self.emb.weight, 0.0, 256 ** -0.5) | |
self.null = torch.LongTensor([0]) | |
self.uncond_ratio = uncond_ratio | |
self.cfg = cfg | |
def infer(self, x_mel, w2v, length, f0): | |
x_mask = torch.unsqueeze(commons.sequence_mask(length, x_mel.size(2)), 1).to(x_mel.dtype) | |
# Speaker embedding from mel (Style Encoder) | |
g = self.emb_g(x_mel, x_mask).unsqueeze(-1) | |
z, _, _ = self.enc_p_l(w2v, f0, x_mask, g=g) | |
z = self.flow_l(z, x_mask, g=g, reverse=True) | |
z = self.flow(z, x_mask, g=g, reverse=True) | |
e, e_ = self.sn(z, g) | |
o = self.dec(z, e, g=g) | |
return o, e_ | |
def voice_conversion(self, src, src_length, trg_mel, trg_length, f0, noise_scale = 0.333, uncond=False): | |
trg_mask = torch.unsqueeze(commons.sequence_mask(trg_length, trg_mel.size(2)), 1).to(trg_mel.dtype) | |
g = self.emb_g(trg_mel, trg_mask).unsqueeze(-1) | |
y_mask = torch.unsqueeze(commons.sequence_mask(src_length, src.size(2)), 1).to(trg_mel.dtype) | |
z, m_p, logs_p = self.enc_p_l(src, f0, y_mask, g=g) | |
z = (m_p + torch.randn_like(m_p) * torch.exp(logs_p)*noise_scale) * y_mask | |
z = self.flow_l(z, y_mask, g=g, reverse=True) | |
z = self.flow(z, y_mask, g=g, reverse=True) | |
if uncond: | |
null_emb = self.emb(self.null) * math.sqrt(256) | |
g = null_emb.unsqueeze(-1) | |
e, _ = self.sn(z, g) | |
o = self.dec(z, e, g=g) | |
return o | |
def voice_conversion_noise_control(self, src, src_length, trg_mel, trg_length, f0, noise_scale = 0.333, uncond=False, denoise_ratio = 0): | |
trg_mask = torch.unsqueeze(commons.sequence_mask(trg_length, trg_mel.size(2)), 1).to(trg_mel.dtype) | |
g = self.emb_g(trg_mel, trg_mask).unsqueeze(-1) | |
g_org, g_denoise = g[:1, :, :], g[1:, :, :] | |
g_interpolation = (1-denoise_ratio)*g_org + denoise_ratio*g_denoise | |
y_mask = torch.unsqueeze(commons.sequence_mask(src_length, src.size(2)), 1).to(trg_mel.dtype) | |
z, m_p, logs_p = self.enc_p_l(src, f0, y_mask, g=g_interpolation) | |
z = (m_p + torch.randn_like(m_p) * torch.exp(logs_p)*noise_scale) * y_mask | |
z = self.flow_l(z, y_mask, g=g_interpolation, reverse=True) | |
z = self.flow(z, y_mask, g=g_interpolation, reverse=True) | |
if uncond: | |
null_emb = self.emb(self.null) * math.sqrt(256) | |
g = null_emb.unsqueeze(-1) | |
e, _ = self.sn(z, g_interpolation) | |
o = self.dec(z, e, g=g_interpolation) | |
return o | |
def f0_extraction(self, x_linear, x_mel, length, x_audio, noise_scale = 0.333): | |
x_mask = torch.unsqueeze(commons.sequence_mask(length, x_mel.size(2)), 1).to(x_mel.dtype) | |
# Speaker embedding from mel (Style Encoder) | |
g = self.emb_g(x_mel, x_mask).unsqueeze(-1) | |
# posterior encoder from linear spec. | |
_, m_q, logs_q= self.enc_q(x_linear, x_audio, x_mask, g=g) | |
z = (m_q + torch.randn_like(m_q) * torch.exp(logs_q)*noise_scale) | |
# Source Networks | |
_, e_ = self.sn(z, g) | |
return e_ | |